Key papers

• Learning dynamical systems with hit-and-run random feature maps (2025)
  Pinak Mandal et al.
  5.79
• Deterministic Reservoir Computing for Chaotic Time Series Prediction (2025)
  Johannes Viehweg et al.
  5.13
• A tensor network approach for chaotic time series prediction (2025)
  Rodrigo Mart\'inez-Pe\~na et al.
  4.82
• Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems (2025)
  Ira J. S. Shokar et al.
  4.76
• A Novel Approach for Estimating Largest Lyapunov Exponents in One-Dimensional Chaotic Time Series Using Machine Learning (2025)
  A. Velichko et al.
  4.53
• Attractor Geometry Determines the Identifiability Limits of System Discovery (2026)
  Matteo Gallo et al.
  4.39
• Online model learning with data-assimilated reservoir computers (2025)
  Andrea N\'ovoa et al.
  3.70
• Dynamical errors in machine learning forecasts (2025)
  Zhou Fang et al.
  2.76
• Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series (2026)
  Madhurima Panja et al.
  1.78
• Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing (2026)
  Xin Li et al.
  1.78
• Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder (2026)
  Kewei Zhu et al.
  1.78
• Continuous-Time Piecewise-Linear Recurrent Neural Networks (2026)
  Alena Br\"andle et al.
  1.72
• Neural CDEs as Correctors for Learned Time Series Models (2025)
  Muhammad Bilal Shahid et al.
  1.61
• Random Controlled Differential Equations (2025)
  Francesco Piatti et al.
  1.61
• A Novel Reservoir Computing Framework for Chaotic Time Series Prediction Using Time Delay Embedding and Random Fourier Features (2025)
  S. K. Laha
  1.56
• Reservoir Computing via Multi-Scale Random Fourier Features for Forecasting Fast-Slow Dynamical Systems (2025)
  S. K. Laha
  1.56
• Modeling Time Series Dynamics with Fourier Ordinary Differential Equations (2025)
  Muhao Guo et al.
  1.50
• Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements (2025)
  Haoran Li and Muhao Guo and Yang Weng and Hanghang Tong
  1.39
• Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods (2025)
  Mahishanka Withanachchi
  1.39
• Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations (2025)
  Eva van Tegelen et al.
  1.33
• Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics (2025)
  Alex Guo et al.
  1.33
• Temporal horizons in forecasting: a performance-learnability trade-off (2025)
  Pau Vilimelis Aceituno et al.
  1.28
• LETS Forecast: Learning Embedology for Time Series Forecasting (2025)
  Abrar Majeedi et al.
  1.28
• Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations (2025)
  Lennart Bastian et al.
  1.28
• SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting (2025)
  Yitian Zhang et al.
  1.28
• FlightKooba: A Fast Interpretable FTP Model (2025)
  Jing Lu et al.
  1.28
• Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems (2025)
  Anton Erofeev et al.
  1.22
• Inference of hidden common driver dynamics by anisotropic self-organizing neural networks (2025)
  Zsigmond Benk\H{o} et al.
  1.17
• Augmented Invertible Koopman Autoencoder for long-term time series forecasting (2025)
  Anthony Frion (Lab-STICC\_OSE et al.
  1.11
• When are dynamical systems learned from time series data statistically accurate? (2024)
  Jeongjin Park and Nicole Yang and Nisha Chandramoorthy
  0.94
• ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting (2024)
  Futoon M.Abushaqra et al.
  0.94
• Latent Space Energy-based Neural ODEs (2024)
  Sheng Cheng et al.
  0.83
• Predicting Chaotic System Behavior using Machine Learning Techniques (2024)
  Huaiyuan Rao et al.
  0.78
• DeNOTS: Stable Deep Neural ODEs for Time Series (2024)
  Ilya Kuleshov et al.
  0.78
• Oscillations enhance time-series prediction in reservoir computing with feedback (2024)
  Yuji Kawai and Takashi Morita and Jihoon Park and Minoru Asada
  0.67
• A novel Reservoir Architecture for Periodic Time Series Prediction (2024)
  Zhongju Yuan et al.
  0.61
• Tree-based Learning for High-Fidelity Prediction of Chaos (2024)
  Adam Giammarese et al.
  0.50
• Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data (2023)
  C.Coelho et al.
  0.06
• Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications) (2016)
  Mahboobeh Parsapoor
  —
• Training Echo State Networks with Regularization through Dimensionality Reduction (2016)
  Sigurd L{\o}kse et al.
  —
• Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model (2016)
  Zhengbing Hu et al.
  —
• Reservoir Computing on the Hypersphere (2017)
  M. Andrecut
  —
• Emulating dynamic non-linear simulators using Gaussian processes (2018)
  Hossein Mohammadi et al.
  —
• Rapid Time Series Prediction with a Hardware-Based Reservoir Computer (2018)
  Daniel Canaday et al.
  —
• Learning low-dimensional state embeddings and metastable clusters from time series data (2019)
  Yifan Sun et al.
  —
• Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting (2019)
  Zachariah Carmichael et al.
  —
• Predicting dynamical system evolution with residual neural networks (2019)
  Artem Chashchin et al.
  —
• Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics (2019)
  Pantelis R. Vlachas et al.
  —
• Dynamic Local Regret for Non-convex Online Forecasting (2019)
  Sergul Aydore et al.
  —
• Forecasting Sequential Data using Consistent Koopman Autoencoders (2020)
  Omri Azencot and N. Benjamin Erichson and Vanessa Lin and Michael W. Mahoney
  —
• Progressive Growing of Neural ODEs (2020)
  Hammad A. Ayyubi et al.
  —
• Memory and forecasting capacities of nonlinear recurrent networks (2020)
  Lukas Gonon et al.
  —
• Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models (2020)
  Futo Tomizawa and Yohei Sawada
  —
• Forecasting Hamiltonian dynamics without canonical coordinates (2020)
  Anshul Choudhary et al.
  —
• Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics (2020)
  Tomoharu Iwata et al.
  —
• Forecasting Using Reservoir Computing: The Role of Generalized Synchronization (2021)
  Jason A. Platt et al.
  —
• Meta-Learning Dynamics Forecasting Using Task Inference (2021)
  Rui Wang et al.
  —
• Weak Form Generalized Hamiltonian Learning (2021)
  Kevin L. Course et al.
  —
• Reconstructing a dynamical system and forecasting time series by self-consistent deep learning (2021)
  Zhe Wang and Claude Guet
  —
• Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics (2021)
  Gouhei Tanaka et al.
  —